At Princeton, It's Not So Easy To Navigate The Quantitative Finance Curriculum

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As the finance industry becomes more competitive and more specialised, preparation for a career as a quant begins earlier and earlier. Students who were quick to discover their interest in quantitative finance demanded programs that would give them the preparation they needed, and universities proved ready and willing to meet that need.

However, while that eagerness to attract students seeking earlier and more advanced preparation created a variety of high-quality programs offering undergrads more advanced financial preparation than ever before, in some cases it also produced a range of choices that was increasingly difficult to navigate.

Evans Xiang, a 2010 Princeton graduate who is now a research analyst at Two Sigma Investments, shared his experiences successfully navigating the range of options to a promising career in quantitative finance and his perspective on choosing a curriculum that provides the necessary level of rigour and the practical applications that prepare students to go straight into a career in the industry.

At Princeton University, undergraduates focused on a career in quantitative finance typically face a decision between the economics, maths and operations research and financial engineering majors, and another dilemma when it comes to certificates, or minors: finance, applied and computational mathematics, and applications of computing.

Finding a balance between theory and practice was the primary difficulty in choosing a curriculum that would send the right signals while also providing real-world skills.

Like many prospective quants, Xiang had originally planned to study maths or physics, but found that those programs too theoretical. Several Princeton students confirmed their notoriously difficult, and even more abstract than equivalent programs at other universities. Professors teaching the introductory analysis course that is traditionally the starting point for maths majors take regular well-meaning jabs at physics for being “too concrete” during lectures, so it’s not hard to imagine the maths department’s assessment of finance.

“The maths program is useful to the extent that it makes you a creative and flexible thinker, but the actual material is irrelevant to the kind of work you will do,” Xiang said.

Although undeniably mathematically talented, Xiang found the more applied operations research and financial engineering (ORFE) program a better fit with his interest in finance. The ORFE curriculum also provides more rigour than economics, with more emphasis on quantitative methods and modelling. He added certificates in applied maths and applications of computing to finish off his academic program.

“ORFE is the closest thing to an applied maths major,” Xiang said. “The certificates complemented my course of study by adding the necessary theory and computer science course work.”

On the whole, however, he said his curriculum “fit pretty well” with what the industry requires, and the amount of overlap meant it wasn’t too difficult to meet all the requirements for the different programs.

While Xiang said he doesn’t know much about the options available at other schools’ undergraduate finance programs, he would have liked to have had the chance to take more advanced courses with a more rigorous treatment of practical topics at the undergraduate level.

“Princeton’s program focused more on theory rather than application, so I had to take some time to learn all the tools used in practice,” Xiang said. “Things in practice are a lot harder than they appear. A lot of subjects taught in the classroom are oversimplified to make it more elegant and easier for us students to grasp.”

Having the opportunity to take graduate school classes at the Bendheim centre was important for Xiang, who found them to be a more mathematically rigorous treatment of the topics he’d been taught in undergraduate-level classes. His graduate-level stochastic calculus course was a favourite, along with an undergraduate version taught by the professor who advised Xiang’s independent work.

Although he considered going to graduate school for applied maths, statistics, or economics, Xiang eventually decided going straight into the industry was the best choice for him. Despite his desire for more rigour in the practical aspects, he thought he was well-prepared – and so did the firm he had interned with the previous summer.

“I had interned at Two Sigma the summer of my junior year and received a full-time offer at the end. A quantitative hedge fund like Two Sigma is the perfect place for someone with my interests and skills,” Xiang explained.

This post originally appeared at Quant Network.

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